saving work

Author: rachfop

.yarnclean

@@ -0,0 +1,45 @@
+# test directories
+__tests__
+test
+tests
+powered-test
+
+# asset directories
+docs
+doc
+website
+images
+assets
+
+# examples
+example
+examples
+
+# code coverage directories
+coverage
+.nyc_output
+
+# build scripts
+Makefile
+Gulpfile.js
+Gruntfile.js
+
+# configs
+appveyor.yml
+circle.yml
+codeship-services.yml
+codeship-steps.yml
+wercker.yml
+.tern-project
+.gitattributes
+.editorconfig
+.*ignore
+.eslintrc
+.jshintrc
+.flowconfig
+.documentup.json
+.yarn-metadata.json
+.travis.yml
+
+# misc
+*.md

docs/sdks/hello/__init__.py

@@ -2,77 +2,203 @@
 title: API
 ---
 
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+This document outlines the core functionalities provided by the RunPod API, including how to interact with endpoints, manage templates, create endpoints, and list available GPUs.
+These operations enable users to dynamically manage their computational resources within the RunPod environment.
+
 ## Get Endpoints
 
-To fetch all available endpoints from the API, use the get_endpoints function.
-This function returns a list of endpoint configurations available for use.
+To retrieve a comprehensive list of all available endpoint configurations within RunPod, you can use the `get_endpoints()` function. This function returns a list of endpoint configurations, allowing you to understand what's available for use in your projects.
 
 ```python
 import runpod
 
+# Fetching all available endpoints
 endpoints = runpod.get_endpoints()
 
+# Displaying the list of endpoints
 print(endpoints)
 ```
 
 ## Create Template
 
-You can create a new template in RunPod by specifying the name and the Docker image to use.
-This is useful for setting up environments with pre-defined configurations.
+Templates in RunPod serve as predefined configurations for setting up environments efficiently. The `create_template()` function facilitates the creation of new templates by specifying a name and a Docker image.
 
 ```python
 import runpod
 
-
 try:
+    # Creating a new template with a specified name and Docker image
+    new_template = runpod.create_template(name="test", image_name="runpod/base:0.1.0")
 
-    new_template = runpod.create_template(
-        name="test",
-        image_name="runpod/base:0.1.0"
-    )
-
+    # Output the created template details
     print(new_template)
 
 except runpod.error.QueryError as err:
+    # Handling potential errors during template creation
     print(err)
     print(err.query)
 ```
 
 ## Create Endpoint
 
-Creating an endpoint involves first creating a template and then setting up the endpoint with the template ID.
-You can specify GPU requirements, the number of workers, and other configurations.
-Your Template name must be unique.
+Creating a new endpoint is straightforward with the `create_endpoint()` function. This function requires specifying a `name` and a `template_id`. Additional configurations such as GPUs, number of Workers, and more can also be specified to tailor the endpoint to your specific needs.
+
+<Tabs>
+  <TabItem value="python" label="Python" default>
 
 ```python
 import runpod
 
 try:
-
+    # Creating a template to use with the new endpoint
     new_template = runpod.create_template(
-        name="test",
-        image_name="runpod/base:0.4.4",
-        is_serverless=True
+        name="test", image_name="runpod/base:0.4.4", is_serverless=True
     )
 
+    # Output the created template details
     print(new_template)
 
+    # Creating a new endpoint using the previously created template
     new_endpoint = runpod.create_endpoint(
         name="test",
         template_id=new_template["id"],
         gpu_ids="AMPERE_16",
         workers_min=0,
-        workers_max=1
+        workers_max=1,
     )
 
+    # Output the created endpoint details
     print(new_endpoint)
 
 except runpod.error.QueryError as err:
+    # Handling potential errors during endpoint creation
     print(err)
     print(err.query)
 ```
 
+</TabItem>
+  <TabItem value="output" label="Output">
+
+```json
+{
+  "id": "Unique_Id",
+  "name": "YourTemplate",
+  "imageName": "runpod/base:0.4.4",
+  "dockerArgs": "",
+  "containerDiskInGb": 10,
+  "volumeInGb": 0,
+  "volumeMountPath": "/workspace",
+  "ports": null,
+  "env": [],
+  "isServerless": true
+}
+{
+  "id": "Unique_Id",
+  "name": "YourTemplate",
+  "templateId": "Unique_Id",
+  "gpuIds": "AMPERE_16",
+  "networkVolumeId": null,
+  "locations": null,
+  "idleTimeout": 5,
+  "scalerType": "QUEUE_DELAY",
+  "scalerValue": 4,
+  "workersMin": 0,
+  "workersMax": 1
+}
+```
+
+</TabItem>
+</Tabs>
+
+## Get GPUs
+
+For understanding the computational resources available, the `get_gpus()` function lists all GPUs that can be allocated to endpoints in RunPod. This enables optimal resource selection based on your computational needs.
+
+<Tabs>
+  <TabItem value="python" label="Python" default>
+
+```python
+import runpod
+import json
+
+# Fetching all available GPUs
+gpus = runpod.get_gpus()
+
+# Displaying the GPUs in a formatted manner
+print(json.dumps(gpus, indent=2))
+```
+
+</TabItem>
+  <TabItem value="output" label="Output">
+
 ```json
-{'id': 'cx829zvv9e', 'name': 'testing-01', 'imageName': 'runpod/base:0.4.4', 'dockerArgs': '', 'containerDiskInGb': 10, 'volumeInGb': 0, 'volumeMountPath': '/workspace', 'ports': None, 'env': [], 'isServerless': True}
-{'id': '838j9id2xmmwew', 'name': 'test', 'templateId': 'cx829zvv9e', 'gpuIds': 'AMPERE_16', 'networkVolumeId': None, 'locations': None, 'idleTimeout': 5, 'scalerType': 'QUEUE_DELAY', 'scalerValue': 4, 'workersMin': 0, 'workersMax': 1}
+[
+  {
+    "id": "NVIDIA A100 80GB PCIe",
+    "displayName": "A100 80GB",
+    "memoryInGb": 80
+  },
+  {
+    "id": "NVIDIA A100-SXM4-80GB",
+    "displayName": "A100 SXM 80GB",
+    "memoryInGb": 80
+  }
+  // Additional GPUs omitted for brevity
+]
 ```
+
+</TabItem>
+</Tabs>
+
+## Get GPU by Id
+
+Use `get_gpu()` and pass in a GPU Id to retrieve details about a specific GPU model by its ID.
+This is useful when understanding the capabilities and costs associated with various GPU models.
+
+<Tabs>
+  <TabItem value="python" label="Python" default>
+
+```python
+import runpod
+import json
+
+gpus = runpod.get_gpu("NVIDIA A100 80GB PCIe")
+
+print(json.dumps(gpus, indent=2))
+```
+
+</TabItem>
+  <TabItem value="output" label="Output">
+
+```json
+{
+  "maxGpuCount": 8,
+  "id": "NVIDIA A100 80GB PCIe",
+  "displayName": "A100 80GB",
+  "manufacturer": "Nvidia",
+  "memoryInGb": 80,
+  "cudaCores": 0,
+  "secureCloud": true,
+  "communityCloud": true,
+  "securePrice": 1.89,
+  "communityPrice": 1.59,
+  "oneMonthPrice": null,
+  "threeMonthPrice": null,
+  "oneWeekPrice": null,
+  "communitySpotPrice": 0.89,
+  "secureSpotPrice": null,
+  "lowestPrice": {
+    "minimumBidPrice": 0.89,
+    "uninterruptablePrice": 1.59
+  }
+}
+```
+
+</TabItem>
+
+</Tabs>
+
+Through these functionalities, the RunPod API enables efficient and flexible management of computational resources, catering to a wide range of project requirements.